Physical sensors are widely used in many products, such as modern machines, to measure and monitor physical phenomena, such as temperature, speed, and emissions from motor vehicles. Physical sensors often take direct measurements of the physical phenomena and convert these measurements into measurement data to be further processed by control systems. Although physical sensors take direct measurements of the physical phenomena, physical sensors and associated hardware are often costly and, sometimes, unreliable. Further, when control systems rely on physical sensors to operate properly, a failure of a physical sensor may render such control systems inoperable. For example, the failure of an intake manifold pressure sensor in an engine may result in shutdown of the engine entirely even if the engine itself is still operable.
Virtual sensors have been developed to process other various physically measured values and to produce values that were previously measured directly by physical sensors. For example, U.S. Pat. No. 6,275,761 (the '761 patent) issued to Ting on Aug. 14, 2001, discloses a neural network-based virtual sensor for automatic transmission slip. The '761 patent uses a composite slip estimator that utilizes different neural network designs for different operating conditions of the vehicle. A specific neural network-based slip estimator design can be tailored to perform well for limited specific sets of powertrain operating conditions. The specific sets are enveloped into a number of distinct subsets, each corresponding to a different slip estimator designed to perform under those specific conditions.
A modern machine may need multiple sensors to function properly, and multiple virtual sensors may be used. However, conventional multiple virtual sensors are often used independently without taking into account other virtual sensors in an operating environment, which may result in undesired results. For example, multiple virtual sensors may compete for limited computing resources, such as processor, memory, or I/O, etc. An output of one virtual sensor model could also inadvertently become an input to another virtual sensor model, which can result in unpredictable effects in complex control systems relying on these values. Further, other types of interactions among the multiple virtual sensors may cause undesired or unpredictable results, such as feedback loops or transient control instabilities.
Further, conventional multiple virtual sensors are often incapable of being calibrated to provide users information, such as sensing ranges, uncertainty, and sensing conditions about the multiple virtual sensors and, more specifically, the multiple virtual sensors as a whole. Conventional multiple virtual sensors are often also incapable of providing auditing and/or publishing functionalities to comply with various standards.
Methods and systems consistent with certain features of the disclosed systems are directed to solving one or more of the problems set forth above.